路网结构建模与车辆轨迹概率计算

Xianbin Zeng, Meiling Wu, Yunguo Lin, Yongxian Wen
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引用次数: 0

摘要

由车辆轨迹生成的超大尺度道路网络具有自相似性、异步并发性、空时性和随机性。在植物根系生长模型及其形态结构建模方法的基础上,对传统的l系统进行了扩展,提出了时空一般传播确定性零边l系统(st - gpd0l系统)的概念。为了弥补路网结构建模的同步性和静态性等缺点,本文利用st - gpd0l系统对路网形状和结构进行建模,并利用st - gpd0l系统生成的语言对车辆轨迹进行描述。同时,基于离散马尔可夫链的数学模型,构建联合系统,将车辆轨迹转化为离散马尔可夫链的状态序列,并利用离散马尔可夫链的转移概率给出车辆轨迹的概率公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural Modeling of Road Network and Probability Calculation of Vehicle Trajectory
The ultra-large-scale road network generated by the vehicle trajectory has self-similarity, asynchronous concurrency, space-time and randomness. Based on the growth model of plant roots and its morphological structure modeling method, the paper extends the traditional L-system and provides the concept of space-time general propagating deterministic zerosided L-system(ST-GPD0L-system). In order to make up for the shortcomings of road network structure modeling, such as synchronization and being static, the paper uses the ST-GPD0L-system to present a model for the shape and structure of the road network and describes the vehicle trajectory by the generated language of the ST-GPD0L-system. Whilst, a joint system is constructed, based on the mathematical model of discrete Markov chain, to transform the vehicle trajectory into a state sequence of discrete Markov chains, and the probability formula of the vehicle trajectory is given by using the transition probability of the discrete Markov chain.
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